A chaos analysis for Greek and Turkish equity markets

Abstract

PurposeThis paper sets out to apply chaos theory to the prediction of stock returns using Greek and Turkish stock index data. The aim of the analysis is to empirically show whether the markets have informational efficiency, in a comparative perspective.Design/methodology/approachThe research employs Grassberger and Procaccia's methodology in the time series analysis in order to estimate the correlation and minimum embedding dimensions of the corresponding strange attractor. To achieve out of the sample multistep ahead prediction, the paper gives the average for overall neighbours' projections of k‐steps into the future.FindingsThe results display the fact that the chaos theory is suitable to examine the time series of stock index returns. The empirical findings show that the stock markets are efficient in Greece, though in Turkey the market is predictable. The main practical implication of the findings is that the technical analysis works in Turkish markets and it is possible to beat the market, while in Greece the fundamental analysis works for equity trading.Originality/valueThe research results have both methodological and practical originality. On the theoretical side, the research shows how the chaos theory can be applied in financial time series analysis. The model is employed with data from Greece, as an EU member; and Turkey, as a candidate to the EU. The fact that the model works in Turkey implies that chaos theory can be used in emerging economies as a prediction model. On the practical side, the paper contributed to the previous literature by providing empirical evidence on market efficiency using a stochastic model.

Deterministic chaos and forecasting in Amazon?s share prices

“…The method of nonlinear analysis had been applied successfully as in the work of Ozun et al (2010). The nonlinear analysis and the prediction of stock returns using Greek and Turkish stock index data had shown empirically whether the markets have informational efficiency, in a comparative perspective (Ozun et al, 2010). Similar results i.e deterministic chaotic behavior, have been found not only in individual stock analyses but also in the behavior of a more global indicator as the S&P index (Hanias et al, 2013).…”

“…This is an indication of chaotic behavior caused by the separation between neighboring vectors within the phase space and is not met when the data follows a random walk process. The method of nonlinear analysis had been applied successfully as in the work of Ozun et al (2010). The nonlinear analysis and the prediction of stock returns using Greek and Turkish stock index data had shown empirically whether the markets have informational efficiency, in a comparative perspective (Ozun et al, 2010).…”

Stock prices' long memory in China and the United States

“…Surprisingly, evidence of long memory in stock market returns is indeed found in North African, European Union, and Asian countries, such as Egypt, Denmark and Singapore, respectively (Boubaker and Makram, 2012; Duppati et al , 2017; Sensoy and Benjamin, 2016). Nevertheless, it has been documented that stock markets are efficient in Greece (Ozun et al , 2010), Malaysia and Thailand (Munir et al , 2012), and Japan and Canada (Ferreira and Andreia, 2014), implying that long memory never occurs in these markets. Therefore, there is no consensus on whether long memory exists in stock markets.…”

Chaotic Bi-LSTM and attention HLCO predictor-based quantum price level fuzzy logic trading system

“…Researchers learnt that many real-world problems and models in financial market contain chaotic attributes [15] [16] [17] [18] that even a predictable, deterministic model or system such as a three-body problem will become unpredictable in its evolution. Therefore, novel designed predictor with algorithm should be capable [24] [25] [26] to learn chaotic attributes or simulate chaos theory and improve prediction in financial market.…”

“…Therefore, we proposed a method by applying advanced artificial neural network [10] [27] [28] [29] and fuzzy logic [30] [31] [32] to cooperate with chaotic neural networks [15] [16] [17] [18] and QPLs [22] [23], so-called Chaotic Bi-LSTM and Attention HLCO Predictor Based Quantum Price Level Fuzzy Logic Trading System to solve these problems.…”